Gemini 3.1 Pro: Mastering Large Context Windows

The Mixture-of-Experts (MoE) Paradigm

• Understanding Sparse Activation in LLMs 45min
• Gemini 1.5 Pro vs. Traditional Dense Models 30min
• Tokenization and Context Window Mechanics 💻 45min

API Integration and Environment Setup

• Google AI Studio and Vertex AI Setup 💻 30min
• Authentication and Rate Limiting Best Practices 💻 30min
• SDK Implementation for Python 💻 45min

Long-Context Retrieval Strategies

• Needle in a Haystack Testing 🔒
• Contextual Compression Techniques 🔒
• Managing Memory and Latency in Long Prompts 🔒

Video and Audio Analysis

• Ingesting Long-form Video for Semantic Search 🔒
• Audio Transcription and Sentiment Analysis 🔒
• Cross-Modal Synchronization Techniques 🔒

System Prompting and Persona Design

• Structuring Massive Contextual Inputs 🔒
• Few-Shot Learning with Large Datasets 🔒
• Chain-of-Thought in Long-Context Scenarios 🔒

When to Use RAG vs. Native Long Context

• Cost Analysis of Token Consumption 🔒
• Hybrid Retrieval-Augmented Generation 🔒
• Evaluating Accuracy and Hallucination Rates 🔒

Operationalizing Gemini 1.5

• Caching Strategies for Repeated Context 🔒
• Monitoring and Observability in Production 🔒
• Security and Data Privacy Compliance 🔒

Project Execution

• Building a Multi-Document Analysis System 🔒
• Integrating Video and Textual Data Sources 🔒
• Final Deployment and Performance Benchmarking 🔒